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Journal of Marketing Research
Vol. XXXVI (August 1999), 356–372

*Amy K. Smith is Assistant Professor of Marketing, George Washington
University (e-mail: [email protected]). Ruth N. Bolton is Ruby K. Powell
Professor of Marketing, Michael F. Price College of Business, University of
Oklahoma (e-mail: [email protected]). Janet Wagner is Associate
Professor of Marketing, The Robert H. Smith School of Business,
University of Maryland (e-mail: [email protected]). The authors
gratefully acknowledge the financial support of the Marketing Science
Institute. To interact with colleagues on specific articles in this issue, see
“Feedback” on the JMR Web site at


Customers often react strongly to service failures, so it is critical that an
organization’s recovery efforts be equally strong and effective. In this ar-
ticle, the authors develop a model of customer satisfaction with service
failure/recovery encounters based on an exchange framework that inte-
grates concepts from both the consumer satisfaction and social justice lit-
erature, using principles of resource exchange, mental accounting, and
prospect theory. The research employs a mixed-design experiment, con-
ducted using a survey method, in which customers evaluate various fail-
ure/recovery scenarios and complete a questionnaire with respect to an
organization they recently had patronized. The authors execute the re-
search in the context of two different service settings, restaurants and ho-
tels.The results show that customers prefer to receive recovery resources
that “match” the type of failure they experience in “amounts” that are
commensurate with the magnitude of the failure that occurs. The findings
contribute to the understanding of theoretical principles that explain cus-
tomer evaluations of service failure/recovery encounters and provide
managers with useful guidelines for establishing the proper “fit” between

a service failure and the recovery effort.

A Model of Customer Satisfaction with
Service Encounters Involving Failure
and Recovery

Organizations are facing more intense customer service
pressures than ever before. When a service failure occurs,
the organization’s response has the potential either to restore
customer satisfaction and reinforce loyalty or to exacerbate
the situation and drive the customer to a competing firm.
Service recovery refers to the actions an organization takes
in response to a service failure (Gronroos 1988). Recovery
management is considered to have a significant impact on
customer evaluations, because customers are usually more
emotionally involved in and observant of recovery service
than in routine or first-time service and are often more dis-
satisfied by an organization’s failure to recover than by the
service failure itself (Berry and Parasuraman 1991; Bitner,
Booms, and Tetreault 1990). Keaveney (1995) finds that

service failures and failed recoveries are a leading cause of
customer switching behavior in service organizations.
Therefore, well-executed service recoveries are important
for enhancing customer satisfaction, building customer rela-
tionships, and preventing customer defections (Fornell and
Wernerfelt 1987).

Although service recovery is recognized by researchers
and managers as a critical element of customer service strat-
egy, there are few theoretical or empirical studies of service
failure and recovery issues. Studying service recovery is
challenging because recovery is triggered by a service fail-
ure, making systematic empirical research difficult to con-
duct in either a laboratory or a field environment. Previous
research on service recovery has focused on developing
classification schemes (Bitner, Booms, and Tetreault 1990;
Hoffman, Kelley, and Rotalsky 1995; Kelley, Hoffman, and
Davis 1993) and providing correlational or anecdotal sup-
port for the effect of service recovery on customer satisfac-
tion (Kelly and Davis 1994; Spreng, Harrell, and Mackoy
1995). Recently, Tax, Brown, and Chandrashekaran (1998)
examined the influence of customers’ justice evaluations on
satisfaction, trust, and commitment after a service complaint
experience. However, to date, no one has developed a theo-
ry-driven model of customer satisfaction with service fail-

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Failure and Recovery 357

ure/recovery encounters that considers proactive recovery
situations, in which the organization initiates a recovery ef-
fort, as well as reactive recovery situations, in which the cus-
tomer’s complaint initiates the recovery effort.

In a recent review article, Rust and Metters (1996) called
for interdisciplinary models of customer behavior in servic-
es marketing. We present an exchange framework for ex-
plaining customer evaluations of service failure/recovery ex-
periences, drawing on behavioral principles of resource
exchange, prospect theory, and mental accounting. From
this framework, we derive a model of customer satisfaction
with service failure/recovery encounters that includes three
dimensions of perceived justice (a social exchange concept),
as well as disconfirmation of expectations (a customer satis-
faction concept). The objectives of this research are to (1)
develop and test a model of customer satisfaction with serv-
ice failure/recovery encounters, using an exchange frame-
work; (2) determine the effects of various types of recovery
efforts on customer evaluations in a variety of service failure
contexts; and (3) provide managers with guidelines for es-
tablishing the proper “fit” between a service failure and the
recovery effort.

Unlike prior studies, our model integrates perceived jus-
tice and expectancy disconfirmation, investigates specific
aspects of the service failure and the recovery effort as an-
tecedents to customer evaluations, and includes proactive
and reactive recovery efforts. We treat service recovery as a
“bundle of resources” that an organization can employ in re-
sponse to a failure. By treating recovery in this manner, we
are able to examine the specific determinants of an effective
recovery and the relative importance of individual recovery
attributes in restoring customer satisfaction across a variety
of service failure conditions. We use a mixed-design exper-
iment, conducted by survey, in which customers evaluate
various service failure/recovery scenarios relative to an or-
ganization they recently had patronized. This research is
performed in the context of two different service settings,
restaurants and hotels.


In the next three sections, we present a model and a set of
hypotheses that describe the effects of service recovery ef-
forts in various failure contexts on customers’ perceptions of
justice and judgments of satisfaction. The model provides a
framework for considering how service failure context (type
and magnitude) and service recovery attributes (compensa-
tion, response speed, apology, initiation) influence customer
evaluations through disconfirmation and perceived justice,
thereby influencing satisfaction with the service failure/re-
covery encounter. The hypotheses describe the effects of
perceived justice on customer satisfaction; of recovery at-
tributes on perceived justice; and of failure context, recovery
attributes, and their interaction on perceived justice.

Effects of Perceived Justice and Disconfirmation on
Customer Satisfaction

Oliver and Swan (1989a, b) were first to model the joint
influence of disconfirmation and perceived justice on cus-
tomer satisfaction, but they address only one aspect of per-
ceived justice, the distributive (equity) aspect. Because the
role of disconfirmation is well known, we focus on the ef-

1This conceptualization was adapted from Bies and Moag (1986) and has
been applied to complaint handling episodes (Tax 1993; Tax, Brown, and
Chandrashekaran 1998). Although the three dimensions originally were
presented as a sequence of events, we do not consider them sequential be-
cause, in practice, many of the exchanges overlap or occur simultaneously.

2Several researchers have considered the influence of perceptions of jus-
tice (fairness) on customer evaluations and behavioral intentions (e.g.,
Blodgett, Granbois, and Walters 1993; Blodgett, Hill, and Tax 1997;
Goodwin and Ross 1992; Tax, Brown, and Chandrashekaran 1998).
However, these studies do not consider the joint influence of perceived jus-
tice and expectancy disconfirmation.

3All hypotheses are stated under ceteris paribus conditions.

fects of perceived justice on customer satisfaction with serv-
ice failure/recovery encounters. Social exchange theorists
have identified three dimensions of perceived justice that in-
fluence how people evaluate exchanges: distributive justice,
which involves resource allocation and the perceived out-
come of exchange (Adams 1965; Deutsch 1975); procedur-
al justice, which involves the means by which decisions are
made and conflicts are resolved (Leventhal 1980; Lind and
Tyler 1988; Thibaut and Walker 1975); and interactional
justice, which involves the manner in which information is
exchanged and outcomes are communicated (Bies and
Moag 1986: Bies and Shapiro 1987). On the basis of the re-
sults of their study involving customers’ perceptions of fair-
ness across four types of service businesses, Clemmer and
Schneider (1996) conclude that customers also evaluate
service encounters on three dimensions: outcome, the bene-
fits (or lack thereof) customers receive as a result of the en-
counter; procedure, the organization’s policies and methods
that guide the encounter; and interaction, the quality of the
interpersonal treatment and communication during the en-

We view a service failure/recovery encounter as a series
of events in which a service failure triggers a procedure that
generates economic and social interaction between the cus-
tomer and the organization, through which an outcome is al-
located to the customer.1 Therefore, we expect that customer
satisfaction with service failure/recovery encounters will be
influenced by customers’ perceptions of all three dimen-
sions of justice—distributive, procedural, and interaction-
al—after controlling for the effects of disconfirmation that
arise from the service encounter.2

H1: In service failure/recovery encounters, customer satisfaction
will be related positively to perceptions of (a) distributive
justice, (b) procedural justice, and (c) interactional justice.3

Effects of Service Failure Context and Recovery Attributes
on Perceived Justice

A service failure/recovery encounter can be viewed as an
exchange in which the customer experiences a loss due to
the failure and the organization attempts to provide a gain,
in the form of a recovery effort, to make up for the cus-
tomer’s loss. This notion is adapted from social exchange
and equity theories (e.g., Homans 1961; Walster, Berscheid,
and Walster 1973; Walster, Walster, and Berscheid 1978).
Service failure/recovery encounters can be considered
mixed exchanges with both utilitarian and symbolic dimen-
sions. Utilitarian exchange involves economic resources,
such as money, goods, or time, whereas symbolic exchange
involves psychological or social resources, such as status,
esteem, or empathy (Bagozzi 1975). Service failures can re-


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Failure and Recovery 363

Table 3


Seven-point scale, anchored at middle and endpoints (“Much Worse than
Expected”/“As Expected”/“Much Better than Expected”). Adapted from
Oliver and Swan (1989a, b).

1. The hotel’s overall response to my problem was .…

Distributive Justice

Seven-point scale, anchored at middle and endpoints (“Strongly
Disagree”/“Neither”/“Strongly Agree”). Adapted from Oliver and Swan
(1989a, b) and Tax (1993).

1. The outcome I received was fair.
2. I did not get what I deserved. (R)
3. In resolving the problem, the hotel gave me what I needed.
4. The outcome I received was not right. (R)

Procedural Justice

Seven-point scale, anchored at middle and endpoints (“Strongly
Disagree”/“Neither”/“Strongly Agree”). Adapted from Tax (1993).

1. The length of time taken to resolve my problem was longer than
necessary. (R)

2. The hotel showed adequate flexibility in dealing with my problem.

Interactional Justice

Seven-point scale, anchored at middle and endpoints (“Strongly
Disagree”/“Neither”/“Strongly Agree”). Adapted from Tax (1993).

1. The employees were appropriately concerned about my problem.
2. The employees did not put the proper effort into resolving my problem.

3. The employees’ communications with me were appropriate.
4. The employees did not give me the courtesy I was due. (R)

Service Encounter Satisfaction

Seven-point scale, anchored at endpoints (“Very Dissatisfied”/“Very
Satisfied”). Adapted from Bitner and Hubbert (1994) and Oliver and
Swan (1989a, b).

1. Think about both the problem you experienced and the hotel’s handling
of the problem. How do you feel about the organization on this
particular occasion?

Notes: (R) = reverse coded.

11The measures displayed high levels of reliability and convergent and
discriminant validity according to conventional assessment procedures
(e.g., Anderson and Gerbing 1988; Churchill 1979; Peter 1979, 1981). For
example, Cronbach’s alphas range from .88 to .93, individual items load on
the proper factors for the three perceived justice constructs, and correlations
among the variables representing different justice constructs are much
smaller than the associated reliabilities. This result is not surprising because
the scales are based on prior research. Additional information about the
measurement scales is available on request.

12The ratings included four self-report measures, taken after the conjoint
task, in which customers evaluated the importance of each of the four re-
covery attributes given the failure they experienced. The measures were
created from these ratings because the ratings were based on the same scale
as the measures of the dependent variables, but they were not predictor vari-
ables in any of the model equations.

Study 1 was conducted in a controlled group setting in
which subjects responded to multiple items for each de-
pendent measure. Study 2 involved a mail survey of business
travelers. We anticipated that the measurement task would
be too time-consuming for these subjects, thereby causing
an adverse effect on response rate. Consequently, the results
from Study 1 were used to identify a subset of reliable and
valid measures for Study 2. In Study 2, each dependent con-
struct was represented by a single-item measure or an index
created from a smaller number of measures (i.e., two to
four) than was used in Study 1. The scale items used in
Study 2 are presented in Table 3.11 Measurement scales
were adapted from previous studies of service encounters,
customer satisfaction, and perceived justice. There were mi-
nor modifications in wording between Study 1 and Study 2
to account for differences in the service contexts.


The mixed experimental design had three features that
needed to be accounted for in the estimation procedure:
scale effects, heteroscedastic disturbances, and individual
differences. As a result, the equations were estimated using
weighted least squares regression. The data were cross-sec-
tional, with each customer evaluating one of four failure sce-
narios and 8 of 24 recovery profiles. To account for the ef-
fect of individual differences in scale use and measurement
artifacts, the mean and coefficient of variation of the self-ex-
plicated importance ratings for each customer were calcu-
lated and used as covariates in all the equations.12 Glesjer’s
(1969) test was used to test for heteroscedasticity of error
terms due to the mixed design (Johnston 1972). This test re-
vealed heteroscedasticity stemming from both the failure
scenarios and the recovery profiles, which indicates that
weighted least squares was the appropriate estimation tech-
nique. We calculated a separate weight for each of the 96
(4 × 24) combinations of failure scenarios and recovery pro-
files by (1) estimating least squares regressions for each of
the equations, (2) grouping the residuals from each equation
by the 96 failure/recovery combinations, and (3) calculating
the population variance for each group. These weights were
used in the final least squares estimation of the equations
(Greene 1993).

Many researchers have suggested that customer evalua-
tions of service encounters may be influenced by prior ex-
perience with the organization, attributions, attitude toward
complaining, and demographic characteristics (e.g., Bitner
1990; Folkes 1984; Folkes, Koletsky, and Graham 1987;
Richins 1987; Singh 1988, 1990; Tax, Brown, and Chan-

drashekaran 1998; Zeithaml, Berry, and Parasuraman 1993).
Therefore, to account for individual differences in prior ex-
perience, attributions, propensity to voice, propensity to ex-
it, demographics, and type of facility, we included a set of
covariates in the equations for both studies, as is described
in Table 4.

Tests of Assumptions

We hypothesize that each of the three dimensions of per-
ceived justice is affected by certain recovery attributes
(H2–H5) and two-way interactions between recovery attrib-
utes and failure context (H6–H7). It is implicitly assumed
that effects not included in the perceived justice equations
are equal to zero or are very small. This assumption was
tested using a series of nested model joint F-tests, in which
full and reduced models were compared (Neter and
Wasserman 1974, p. 88). The results indicate that for both
studies, two sets of additional variables should be included

Page 9


Table 4

Study 1 Study 2
(Restaurants) (Hotels) Variable Descriptions

Scale Effects
IMPMEAN IMPMEAN Mean of self-explicated importance ratings
IMPCVAR IMPCVAR Coefficient of variation of self-explicated importance ratings

Prior Experiences
YEARS YEARS Number of years respondent has been a customer of the organization
LOYALa LOYAL Self-reported customer loyalty measure (seven-point scale)
STABLE STABLE Stability attribution—likelihood of a similar failure occurring again
WRKPRIOR CLUB Predisposition to respond differently

Study 1: have worked in a restaurant before (yes = 1, no = 0)
Study 2: member of hotel’s frequent traveler club (yes = 1, no = 0)

n/a EXPPROB Prior experience—have experienced service problems before at this hotel

Controllability Attributions
n/a ATTRIB Controllability attributions—index of two self-reported attribution

measures (PREVENT is likelihood that hotel could have prevented the
problem, and CONTROL is degree of control hotel had over the problem)

Propensity to Voice/Exit
PROPCOMP PROPCOMP Propensity to complain—index of four (Study 1) or five (Study 2) items
PROPEXIT PROPEXIT Propensity to exit—single item (Study 1) or index of two items (Study 2)

Type of Facility
RESTYPE HOTTYPE Type of facility: Study 1 used average cost per person as a proxy for

the type of restaurant (in terms of expense); Study 2 used a constant
and three dummy variables (HOTAIR, HOTMET, and HOTSUB) to
represent four types of hotels: airport, metro, suburban, and other
(downtown and expressway)

AGE AGE Age in years

aFor restaurants, LOYAL was modeled as LOYAL1 and LOYAL2, where LOYAL1 represents customers’ self-reported loyalty ratings for the group of cus-
tomers who had visited the named restaurant before and LOYAL2 is a dummy variable created to capture the mean shift in LOYAL1 associated with the group
of respondents who were instructed to skip the loyalty question because it was their first visit to the named restaurant.

Notes: Correlations among the set of covariates were checked to ensure that admitting covariates into the equations would not lead to problems of collinear-
ity in the model. After controlling for scale effects, the set of covariates accounted for less than 8% of the variance explained in each of the model equations
for both restaurants and hotels.

13In the distributive justice equations, some other interaction terms were
significant (p < .05) and were retained in the final model equations.
Specifically, two additional two-way interaction terms were included in the
DJUST equation for the restaurant study, and three additional two-way in-
teraction terms were included in the DJUST equation for the hotel study
(see Table 6).

in each of the perceived justice equations. The first set was
composed of the main effects of the “other” recovery attrib-
utes (i.e., the remaining attributes not specifically included
in a particular hypothesis); the second set was composed of
the two-way interactions of the hypothesized recovery at-
tribute(s) with the other (remaining) recovery attributes.
(See “Other Interaction Effects” in Tables 6–8.) The effects
of all other groups of two- and three-way interactions were
not statistically significant.13

Another assumption of the model is that performance (in
terms of the recovery attributes) operates only indirectly on
satisfaction through disconfirmation and perceived justice.
This assumption was tested by conducting a nested model
joint F-test on the service encounter satisfaction equation
that compared the full model (i.e., the model including both
the effects of disconfirmation and perceived justice and the
effects of the recovery attributes) with the reduced model
(i.e., the model including only the effects of disconfirmation
and perceived justice). For both restaurants and hotels, the

14As a check, reverse F-tests were conducted by comparing the full mod-
el with a reduced model, which included only the effects of the recovery at-
tributes, to confirm that the effects of disconfirmation and perceived justice
contributed significantly to the explanatory power of the model. For both
restaurants and hotels, the results of these joint F-tests indicated that these
effects were needed in the service encounter satisfaction equation.

results of the joint F-tests indicated that the added parame-
ters associated with the recovery attributes did not con-
tribute significantly to the explanatory power of the model
and, therefore, should not be included in the service en-
counter satisfaction equation. This result supports the as-
sumption that the effects of recovery performance do not in-
fluence satisfaction directly but operate only indirectly
through disconfirmation and perceived justice.14


The results for Studies 1 and 2 appear in Tables 5–8. Most
of the proposed relationships are supported in both the
restaurant and the hotel contexts. The overall fit of the mod-
el is good, considering that the equations were estimated
with cross-sectional data resulting from a mixed-design ex-
periment and collected, in part, in a field setting. The R2 val-
ues for the service encounter satisfaction equations (see
Table 5) are .76 for restaurants and .78 for hotels, and the R2
values for the perceived justice equations (see Tables 6–8)

Page 16

Failure and Recovery 371

ing to check in. When you get to your room, you find that
the room is already occupied by another guest. You have to
return to the front desk to be assigned to another room be-
cause there is only one clerk on duty. When you finally get
to your new room, you call the front desk to ask for direc-
tions to a dinner meeting. The representative puts you on
hold and never comes back on the line.

Service Recovery Profile Manipulations (Restaurants and

Service recovery profiles were virtually identical for hotel
and restaurant customers. We show the recovery attribute
statements for restaurant customers, with the words or phras-
es for hotel customers in parentheses. For example, restau-
rant customers read “You are given a 50% discount off your
total bill,” whereas hotel customers read “You are given a
certificate for a 100% discount off your one night’s room
bill. ” Italics are used herein to emphasize the difference be-
tween the two statements but were not seen by respondents.

The waiter (hotel employee) acknowledges the problem

without your having to complain versus you complain about
the problem.

Speedy/Delayed Response:
You immediately receive the following response versus

after 15 (20) minutes, you receive the following response.
Apology/No Apology:
You are offered an apology versus you are not offered an

High/Medium/Low Compensation:
You are given a (certificate for a) 50 (100)% discount off

your total (one night’s room) bill versus you are given a (cer-
tificate for a) 20 (50)% discount off your total (one night’s
room) bill versus you are given no (certificate for a) discount
off your total (one night’s room) bill.


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Reprinted with permission from Journal of Marketing Research, published by the American Marketing Association.

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